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Boost your business systems’ reliability with early problem detection

Easily embed anomaly detection capabilities into your apps so users can quickly identify problems. Through an API, Anomaly Detector Preview ingests time-series data of all types and selects the best-fitting detection model for your data to ensure high accuracy. Customize the service to detect any level of anomaly and deploy it where you need it most -- from the cloud to the intelligent edge with containers. Azure is the only major cloud provider that offers anomaly detection as an AI service.

Powerful inference engine looks at your time-series data set and automatically selects the right algorithm to maximise accuracy for your scenario

Automatic detection eliminates the need for labeled training data. Save time and stay focused on fixing problems as soon as they surface

Fast-track your problem solving with simple setup in the Azure portal and real-time anomaly detection. All it takes is three lines of code.

Detect problems for virtually any scenario

There are many types of time-series data—and no one algorithm fits them all. Anomaly Detector looks at your time-series data set and automatically selects the best algorithm from the model gallery to ensure high accuracy for your specific scenario: business incidents, monitoring IoT device traffic, managing fraud, responding to changing markets and more.

Trusted by Microsoft Azure, Office, Windows and Bing

Monitor your product and service health and deliver reliable customer experiences, using the same anomaly detection service that more than 200 Microsoft product teams rely on.

"Anomaly Detector has been pivotal in supporting our customers, monitoring their business metrics 24 hours a day, 7 days a week. Already we've used the service to alert hundreds of businesses to changes in their data, ranging from e-commerce conversion rates to revenue alerts. Our customers get an enriched view of their analytics, giving them early opportunity to act on their data – an opportunity that they never had previously."